Randomization algorithms for large sparse networks

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Randomization algorithms for large sparse networks. / Puolamäki, Kai; Henelius, Andreas; Ukkonen, Antti.

julkaisussa: Physical Review E, Vuosikerta 99, Nro 5, 053311, 30.05.2019, s. 1-15.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Puolamäki, Kai ; Henelius, Andreas ; Ukkonen, Antti. / Randomization algorithms for large sparse networks. Julkaisussa: Physical Review E. 2019 ; Vuosikerta 99, Nro 5. Sivut 1-15.

Bibtex - Lataa

@article{cfdc389c54f54179bc5ac1b4c6b8eeae,
title = "Randomization algorithms for large sparse networks",
abstract = "In many domains it is necessary to generate surrogate networks, e.g., for hypothesis testing of different properties of a network. Generating surrogate networks typically requires that different properties of the network are preserved, e.g., edges may not be added or deleted and edge weights may be restricted to certain intervals. In this paper we present an efficient property-preserving Markov chain Monte Carlo method termed CycleSampler for generating surrogate networks in which (1) edge weights are constrained to intervals and vertex strengths are preserved exactly, and (2) edge and vertex strengths are both constrained to intervals. These two types of constraints cover a wide variety of practical use cases. The method is applicable to both undirected and directed graphs. We empirically demonstrate the efficiency of the CycleSampler method on real-world data sets. We provide an implementation of CycleSampler in R, with parts implemented in C.",
author = "Kai Puolam{\"a}ki and Andreas Henelius and Antti Ukkonen",
year = "2019",
month = "5",
day = "30",
doi = "10.1103/PhysRevE.99.053311",
language = "English",
volume = "99",
pages = "1--15",
journal = "Physical Review E",
issn = "2470-0045",
publisher = "American Physical Society",
number = "5",

}

RIS - Lataa

TY - JOUR

T1 - Randomization algorithms for large sparse networks

AU - Puolamäki, Kai

AU - Henelius, Andreas

AU - Ukkonen, Antti

PY - 2019/5/30

Y1 - 2019/5/30

N2 - In many domains it is necessary to generate surrogate networks, e.g., for hypothesis testing of different properties of a network. Generating surrogate networks typically requires that different properties of the network are preserved, e.g., edges may not be added or deleted and edge weights may be restricted to certain intervals. In this paper we present an efficient property-preserving Markov chain Monte Carlo method termed CycleSampler for generating surrogate networks in which (1) edge weights are constrained to intervals and vertex strengths are preserved exactly, and (2) edge and vertex strengths are both constrained to intervals. These two types of constraints cover a wide variety of practical use cases. The method is applicable to both undirected and directed graphs. We empirically demonstrate the efficiency of the CycleSampler method on real-world data sets. We provide an implementation of CycleSampler in R, with parts implemented in C.

AB - In many domains it is necessary to generate surrogate networks, e.g., for hypothesis testing of different properties of a network. Generating surrogate networks typically requires that different properties of the network are preserved, e.g., edges may not be added or deleted and edge weights may be restricted to certain intervals. In this paper we present an efficient property-preserving Markov chain Monte Carlo method termed CycleSampler for generating surrogate networks in which (1) edge weights are constrained to intervals and vertex strengths are preserved exactly, and (2) edge and vertex strengths are both constrained to intervals. These two types of constraints cover a wide variety of practical use cases. The method is applicable to both undirected and directed graphs. We empirically demonstrate the efficiency of the CycleSampler method on real-world data sets. We provide an implementation of CycleSampler in R, with parts implemented in C.

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U2 - 10.1103/PhysRevE.99.053311

DO - 10.1103/PhysRevE.99.053311

M3 - Article

VL - 99

SP - 1

EP - 15

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 5

M1 - 053311

ER -

ID: 35132749